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This is very interesting, but unfortunately I haven't had the greatest luck in finding new songs I would enjoy listening to. It absolutely finds similar soundin

This is very interesting, but unfortunately I haven't had the greatest luck in finding new songs I would enjoy listening to. It absolutely finds similar sounding tracks, but it doesn't distinguish which part of the song made it enjoyable. There's no tempo consistency or genre consistency or even main instrument/vocal timbre consistency between recommendations. I think locking one or more of those dimensions would allow for much better recommendations. I'm not sure what aspect you're using to order the results, but having extra metadata to filter or group the results in some way would help a lot. Take Raga's Dance by Vanessa-Mae, A R Rahman, ... Royal Philharmonic https://maroofy.com/songs/476841571 https://maroofy.com/songs/476841571 . I put in this track expecting other fusion songs to pop up, and arguably some do, but much more often it feels like a 20 second section was used to define the original song and it misses the underlying concept. Like it got, in my subjective description, the epic violin in orchestral music, but it completely ignores the fusion between the distict styles of traditional indian singing/instrumentals and western ochestral and also ignores the call response structure between the violin and carnatic players, which is the what I actually care about. Other songs have the vocals but no epic backing. It feels like it's matching multiple samples from the song instead of the whole song. This feels very promising since it clearly is picking up the styling of the specific songs across different genres and languages. I look forward to seeing where this goes. I also think it would be interesting if there was a way to specify two different songs to find either only the common things and/or to find what the fusion of those two tracks produces.

dmitriid 4y ago
Categorising music is surprisingly different. See this paper from https://everynoise.com/ https://everynoise.com/ : https://everynoise.com/EverynoiseIntro.pdf https://everynoise.com/EverynoiseIntro.pdf IIRC they try to classify music on 17 different points/features. What you see on the web is an attenpt to visualise (and provide a guide to music based on) some of them
- vintermann 4y ago
  Yes. I think many of those features are based on pre-NN feature detectors (such as BPM), and Danceability, Valence and Energy sound like primary components that have been given names. Echo nest was great for its time, but if they have kept up, they're not exposing their more modern learned features to users anymore.
  - dmitriid 4y ago
    They were acquired by Spotify, and there's been some work done by/for Spotify since then. I'm not at liberty to say what, sadly, as I work for Spotify. I think I can say that one of the main challenges is running this analysis for users. It's prohibitively expensive (or was prohibitively expensive) to use this to keep track of and run recommendations for what users are listening for each user. It can be used on smaller scales, but, well, it's probably NDA :)
    - pbronez 4y ago
      A distributed, local-first architecture much work well for this. I’m happy for my computer to crunch away on my behalf, generating recommendations and indexing stuff. I’m happy to recontribute that work to a common index of some kind. I def prefer for that common index to have a permissive license though!
    - macrolime 4y ago
      Can you say why Spotify's recommendations are so bad? Something like what OP has made should have been relatively simple to make for Spotify for many, many years already, yet that hasn't happen. Is the whole system just rigged to only recommended a few "sponspored" artists?
      - vintermann 4y ago
        I doubt that "why does your product suck" is one of the things a Spotify employee is allowed to talk freely about in public! But I've been watching them, I will speculate. A few years ago, Spotify had two young interns, Sander Dieleman and Aäron van den Oord. We know a bit of what they worked on, because Dieleman blogged on it, and indeed it was something a lot like what OP has made here - only better, I would say. I asked him, and Dieleman was allowed to say that the thing they built was one of the inputs into the then-new Discover Weekly, which made headlines for how outrageously good it was. But Dieleman and v.d.Oord did not stay at Spotify. They were headhunted by DeepMind, and have had a VERY impressive track record there over the years. And I wonder why. Was there a conflict between the old school ML of the Echo Nest people and the new fancy neural net kids? Or was it just, as GP alludes to, that the NN methods were just too computationally expensive and they failed to justify their costs to leadership?
msla 4y ago
It doesn't seem to find similar-sounding tracks at all for me. Examples: The Oblio Joes - "Captain of the Moon" The Bondage Fairies - "Levenus Supremus" ... both chosen so "Just shove a bunch of recent pop-rock at the user" won't work.
subtech 4y ago
Hey thanks for the feedback! I definitely have a lot of improvement to do on the model, it currently performs better for some styles/genres of music than others. But the model architecture I'm using is kinda outdated as well, gotta iterate on it more to improve it further! I'm also thinking of letting users upvote/downvote results, which can also help improve quality on the ranking side.
- thegabriele 4y ago
  I don't know if someone already said this, but as an amateur music producer i would love to upload my songs and discover similarities. Thanks for this Amazing tool
- mustacheemperor 4y ago
  I'll note my own experience, that Spotify and Apple Music both struggle to find me latin reggaeton outside a small subset of popular artists, and my first couple searches with this tool have found me so much music I've never heard before that matches exactly the 'vibe' I want to hear, and is introducing me to different-but-related sounds and artists I couldn't have found on my own. I agree with the other commenter - this is huge for me. Please, do whatever you need to do to monetize this so it never goes away. I would love to pay you for this.
- runnerup 4y ago
  Honestly it's loads better than current Spotify/YouTube Music suggestions. Mostly they just seem to suggest popular stuff that's heavily marketed...even though I seeded all my "thumbs up" with only eclectic stuff. Yes, it's hard to find a song I really really like, but 1-in-10 seem to be something I'd add to my eclectic "thumbs up" playlist. And almost none of them are by any artist that I've heard of before. This is huge for me. Thanks.
  - chiefalchemist 4y ago
    You're not alone. For me, Spotify suggestions are "things you won't hate." Most everything is palatable, but forgettable and too usually not all that interesting.
brentis 4y ago
I had the same experience. Could see the element which it matched with, beat, pitch, etc.but missed the riff or nuance that made the source song special to me
Abecid 4y ago
Same here
dbtc 4y ago
I am enjoying Raga's Dance, which is nothing like what I was just listening to. Thank you for the recommendation ;)
anagram666 4y ago
If I am not mistaken, it this is only trained on the preview and not on the entire song. If you listen to a music with a real intro, it gives strange results. For example: "Goodbye Blue Sky - Pink Floyd" (https://maroofy.com/songs/1065976153 https://maroofy.com/songs/1065976153)
- microtonal 4y ago
  Same for "Station by Station - David Bowie" -- lot's of tracks with ambient noise.
zuminator 4y ago
I agree with everyone's criticisms that it seems to identify similar tempo and melodic riff, irrespective of genre. But to me this is a feature, not a bug. I could see this or something like it opening my eyes to music I would never possibly have found on my own. I really like it! Spotify on the other hand seems to want to send me to the same group of artists and tracks I've listened to before, following some Collatz conjecture type algorithm that eventually converges on the same tuned playlist for that genre, no matter what the starting parameters may be.
- Dwolb 4y ago
  It’s a pretty cool idea and gets to a philosophical question really quick “what do people mean when they say they like similar music?” Era? Artist? Genre? Sound? Tempo? Personally I spend my time finding similar-era music because I like to hear how sounds evolved.
  - zuminator 4y ago
    Ideally one would like an algorithm to be able to realize,"this person prefers to explore new music from the same era," vs "that person prefers to jump around to different countries," vs "the other person prefers to remix their existing playlists," and thus come up with the optimal degree of novelty for each listener. Or at least let the user set a novelty slider to customize their own experience.
- criddell 4y ago
  Spotify wants you to listen to the tracks that they are paid to promote.
- f1refly 4y ago
  > But to me this is a feature, not a bug. I could see this or something like it opening my eyes to music I would never possibly have found on my own. What makes it different from a big "play me a random song" button then?
  - mejutoco 4y ago
gradascent 4y ago
I've only tried a few songs but they've mostly been bangers! I did come across a couple examples where the recommended songs just heavily sampled the original but overall very impressed.

dmitriid 4y ago

Categorising music is surprisingly different. See this paper from https://everynoise.com/ https://everynoise.com/ : https://everynoise.com/EverynoiseIntro.pdf https://everynoise.com/EverynoiseIntro.pdf IIRC they try to classify music on 17 different points/features. What you see on the web is an attenpt to visualise (and provide a guide to music based on) some of them

vintermann 4y ago
Yes. I think many of those features are based on pre-NN feature detectors (such as BPM), and Danceability, Valence and Energy sound like primary components that have been given names. Echo nest was great for its time, but if they have kept up, they're not exposing their more modern learned features to users anymore.
- dmitriid 4y ago
  They were acquired by Spotify, and there's been some work done by/for Spotify since then. I'm not at liberty to say what, sadly, as I work for Spotify. I think I can say that one of the main challenges is running this analysis for users. It's prohibitively expensive (or was prohibitively expensive) to use this to keep track of and run recommendations for what users are listening for each user. It can be used on smaller scales, but, well, it's probably NDA :)
  - pbronez 4y ago
    A distributed, local-first architecture much work well for this. I’m happy for my computer to crunch away on my behalf, generating recommendations and indexing stuff. I’m happy to recontribute that work to a common index of some kind. I def prefer for that common index to have a permissive license though!
  - macrolime 4y ago
    Can you say why Spotify's recommendations are so bad? Something like what OP has made should have been relatively simple to make for Spotify for many, many years already, yet that hasn't happen. Is the whole system just rigged to only recommended a few "sponspored" artists?
    - vintermann 4y ago
      I doubt that "why does your product suck" is one of the things a Spotify employee is allowed to talk freely about in public! But I've been watching them, I will speculate. A few years ago, Spotify had two young interns, Sander Dieleman and Aäron van den Oord. We know a bit of what they worked on, because Dieleman blogged on it, and indeed it was something a lot like what OP has made here - only better, I would say. I asked him, and Dieleman was allowed to say that the thing they built was one of the inputs into the then-new Discover Weekly, which made headlines for how outrageously good it was. But Dieleman and v.d.Oord did not stay at Spotify. They were headhunted by DeepMind, and have had a VERY impressive track record there over the years. And I wonder why. Was there a conflict between the old school ML of the Echo Nest people and the new fancy neural net kids? Or was it just, as GP alludes to, that the NN methods were just too computationally expensive and they failed to justify their costs to leadership?

msla 4y ago

It doesn't seem to find similar-sounding tracks at all for me. Examples: The Oblio Joes - "Captain of the Moon" The Bondage Fairies - "Levenus Supremus" ... both chosen so "Just shove a bunch of recent pop-rock at the user" won't work.

subtech 4y ago

Hey thanks for the feedback! I definitely have a lot of improvement to do on the model, it currently performs better for some styles/genres of music than others. But the model architecture I'm using is kinda outdated as well, gotta iterate on it more to improve it further! I'm also thinking of letting users upvote/downvote results, which can also help improve quality on the ranking side.

thegabriele 4y ago
I don't know if someone already said this, but as an amateur music producer i would love to upload my songs and discover similarities. Thanks for this Amazing tool
mustacheemperor 4y ago
I'll note my own experience, that Spotify and Apple Music both struggle to find me latin reggaeton outside a small subset of popular artists, and my first couple searches with this tool have found me so much music I've never heard before that matches exactly the 'vibe' I want to hear, and is introducing me to different-but-related sounds and artists I couldn't have found on my own. I agree with the other commenter - this is huge for me. Please, do whatever you need to do to monetize this so it never goes away. I would love to pay you for this.
runnerup 4y ago
Honestly it's loads better than current Spotify/YouTube Music suggestions. Mostly they just seem to suggest popular stuff that's heavily marketed...even though I seeded all my "thumbs up" with only eclectic stuff. Yes, it's hard to find a song I really really like, but 1-in-10 seem to be something I'd add to my eclectic "thumbs up" playlist. And almost none of them are by any artist that I've heard of before. This is huge for me. Thanks.
- chiefalchemist 4y ago
  You're not alone. For me, Spotify suggestions are "things you won't hate." Most everything is palatable, but forgettable and too usually not all that interesting.

brentis 4y ago

I had the same experience. Could see the element which it matched with, beat, pitch, etc.but missed the riff or nuance that made the source song special to me

Abecid 4y ago

Same here

dbtc 4y ago

I am enjoying Raga's Dance, which is nothing like what I was just listening to. Thank you for the recommendation ;)

anagram666 4y ago

If I am not mistaken, it this is only trained on the preview and not on the entire song. If you listen to a music with a real intro, it gives strange results. For example: "Goodbye Blue Sky - Pink Floyd" (https://maroofy.com/songs/1065976153 https://maroofy.com/songs/1065976153)

microtonal 4y ago
Same for "Station by Station - David Bowie" -- lot's of tracks with ambient noise.

zuminator 4y ago

I agree with everyone's criticisms that it seems to identify similar tempo and melodic riff, irrespective of genre. But to me this is a feature, not a bug. I could see this or something like it opening my eyes to music I would never possibly have found on my own. I really like it! Spotify on the other hand seems to want to send me to the same group of artists and tracks I've listened to before, following some Collatz conjecture type algorithm that eventually converges on the same tuned playlist for that genre, no matter what the starting parameters may be.

Dwolb 4y ago
It’s a pretty cool idea and gets to a philosophical question really quick “what do people mean when they say they like similar music?” Era? Artist? Genre? Sound? Tempo? Personally I spend my time finding similar-era music because I like to hear how sounds evolved.
- zuminator 4y ago
  Ideally one would like an algorithm to be able to realize,"this person prefers to explore new music from the same era," vs "that person prefers to jump around to different countries," vs "the other person prefers to remix their existing playlists," and thus come up with the optimal degree of novelty for each listener. Or at least let the user set a novelty slider to customize their own experience.
criddell 4y ago
Spotify wants you to listen to the tracks that they are paid to promote.
f1refly 4y ago
> But to me this is a feature, not a bug. I could see this or something like it opening my eyes to music I would never possibly have found on my own. What makes it different from a big "play me a random song" button then?
- mejutoco 4y ago

gradascent 4y ago

I've only tried a few songs but they've mostly been bangers! I did come across a couple examples where the recommended songs just heavily sampled the original but overall very impressed.